Conventionally, for example, an image processing apparatus is designed to read out every line of an image signal generated by a sensor in which a plurality of photoelectric converters are arrayed, so that a high-precision image can be formed by removing shading data from the read-out image signal.
For example, in an image authentication apparatus such as a scanner, more accurate authentication is achieved by removing non-uniformities, in other words shading, within the output unique to that apparatus using a correction circuit.
It should be noted that there are actually two types of shading. Specifically, there is dark shading due to photoelectric converter noise and unevenness in the dark output with respect to a reference level. Additionally, there is light shading due to the light source, the optical system and/or unevenness in the sensitivity of the photoelectric converters as well as the form and reflectivity of the subject.
In order to correct these types of shading, shading correction data is stored in the apparatus in advance as default values or shading correction data is produced by sensing a white reference member prior to a main image sensing operation.
However, a drawback of the conventional art is that the photoelectric converters produce shading data for all lines of the photoelectric converters, so it takes a great deal of time to produce the shading data when there are a large number of photoelectric converters.
Additionally, the above-described shading correction method requires a memory having a very large capacity in which to store the data needed for shading correction, particularly when correcting shading across the entire screen, increasing the number of calculations involved and thus increasing the scale of the circuitry and the cost as well.
Moreover, the conventional art suffers from the addition drawback that storing pre-set default correction values or acquiring a white reference member cannot correct for the uneven lighting that occurs in subject verification systems such as object recognition and fingerprint authentication systems due to the shape of the subject, its reflectivity, its positioning and ambient light conditions.